No packages match

logKDE - Computing Log-Transformed Kernel Density Estimates for Positive Data

Computes log-transformed kernel density estimates for positive data using a variety of kernels. It follows the methods described in Jones, Nguyen and McLachlan (2018) <doi:10.21105/joss.00870>.

Last updated

cpp

4.11 score 2 stars 13 scripts 287 downloads

lowmemtkmeans - Low Memory Use Trimmed K-Means

Performs the trimmed k-means clustering algorithm with lower memory use. It also provides a number of utility functions such as BIC calculations.

Last updated

openblascppopenmp

3.48 score 2 dependents 7 scripts 196 downloads

BoltzMM - Boltzmann Machines with MM Algorithms

Provides probability computation, data generation, and model estimation for fully-visible Boltzmann machines. It follows the methods described in Nguyen and Wood (2016a) <doi:10.1162/NECO_a_00813> and Nguyen and Wood (2016b) <doi:10.1109/TNNLS.2015.2425898>.

Last updated

openblascpp

3.00 score 1 stars 20 scripts 201 downloads

SAGMM - Clustering via Stochastic Approximation and Gaussian Mixture Models

Computes clustering by fitting Gaussian mixture models (GMM) via stochastic approximation following the methods of Nguyen and Jones (2018) <doi:10.1201/9780429446177>. It also provides some test data generation and plotting functionality to assist with this process.

Last updated

openblascppopenmp

2.70 score 5 scripts 189 downloads

SSOSVM - Stream Suitable Online Support Vector Machines

Soft-margin support vector machines (SVMs) are a common class of classification models. The training of SVMs usually requires that the data be available all at once in a single batch, however the Stochastic majorization-minimization (SMM) algorithm framework allows for the training of SVMs on streamed data instead Nguyen, Jones & McLachlan(2018)<doi:10.1007/s42081-018-0001-y>. This package utilizes the SMM framework to provide functions for training SVMs with hinge loss, squared-hinge loss, and logistic loss.

Last updated

openblascpp

2.70 score 6 scripts 200 downloads

EMMIXgene - A Mixture Model-Based Approach to the Clustering of Microarray Expression Data

Provides unsupervised selection and clustering of microarray data using mixture models. Following the methods described in McLachlan, Bean and Peel (2002) <doi:10.1093/bioinformatics/18.3.413> a subset of genes are selected based one the likelihood ratio statistic for the test of one versus two components when fitting mixtures of t-distributions to the expression data for each gene. The dimensionality of this gene subset is further reduced through the use of mixtures of factor analyzers, allowing the tissue samples to be clustered by fitting mixtures of normal distributions.

Last updated

openblascpp

1.70 score 9 scripts 189 downloads